Mobile devices (e.g., cellular phones) are packed with sensors such as accelerometers, magnetometers, gyroscopes, pressure, temperature, light (ALS, RGB, etc.) and proximity sensors, Bluetooth and Wi-Fi radios, GPS, a microphone, camera, etc. These sensors can be leveraged to collect contextual data about the user's actions and surroundings. This data provides an opportunity for mobile devices to become truly smart devices by gaining the ability to understand and leverage the context to intelligently enhance the user experience. Examples of such enhancements are personalization of user interaction (e.g., by adapting the interaction due to preferences) or altering the interaction based on the user context (e.g., adapting the interaction model, modality, or content based on the activity or situation of the user). Moreover, as devices are typically within close proximity to the user, context is available on demand, which is not the case for other computing platforms. Devices could automatically learn to adapt properties based on the situation or activity of users such as “put device on vibrate at dinner” or “enable Bluetooth when leaving home”. This illustrates how user behavior models (e.g. preferences of the user) in combination with contextual awareness (e.g. at a restaurant, in a meeting, or in a conversation) can be combined to enhance the user experience. This opens the flood gates to a range of potential applications that enhance the user experience.
A step towards truly smart devices is context awareness. In one aspect “context awareness” refers to any information that can be used to characterize the situation of the user. Context awareness may allow the discovery of answers to one or more of the following questions: (1) What is the user doing?; (2) What environment are they in?; (3) Where are they?; and (4) What are their intentions? Contextual awareness is inherently dependent on two things: (a) discovery of relevant states; and (b) recognition of state occurrences (e.g. motion states, device positions, places, ambiences, or activities). In one aspect, a system can be still be context aware even if the human is in the loop providing support in discovery, recognition, or both. For example, some location-based services rely on humans to discover relevant states (places), e.g. annotating relevant places on a map, and provide the ability to recognize state occurrences (revisits). Systems based on motion-states or based on ambience states are further examples where the state discovery is often supervised by humans and the ability of recognition provided by the system.
One type of context is the place of a user. A “place” may refer to a physical location with some regional expansion. In terms of the regional expansion, notions of a macro place may be differentiated from those of a micro place. A macro place refers to a place on the level of buildings or blocks, e.g. home, work, mall, or park, while a micro place refers to a place on the level of rooms, e.g. living room, office, men's section in a department store. The relevance of a place often depends on the activity and intent of a user. A relevant place to a user might be the restaurant at which a user has dinner. However, the same restaurant might not be a relevant place at all times. Consider the same user passing by the restaurant going for a walk. The restaurant may no longer be relevant as it is not semantically meaningful with respect to the current activity, namely going for a walk. Identifying which places are relevant is a very challenging problem and difficult to define without a specific application or use case in mind.
Often, machine learning algorithms are applied to run in the background of the mobile device in an always-on mode to continuously identify user context. These algorithms are becoming more and more complex and elaborate, with ever increasing number of context states and requirements for improved accuracy (precision and recall figures) of user context identification. This trend towards more complex contextual awareness algorithms in mobile devices is adversely affecting power consumption.